Price Is King

While this may not be true for every industry, for retail
e-commerce, it's an axiom. Every competitor is a click away and
comparing offerings has never been easier. With massive product
selections and global shipping, differentiating yourself from the
competition has never been harder. Winning on price has never been
more important.

Our Background

RJB has been designing and implementing e-commerce pricing analysis
solutions since 2010. Over that period, we've also kept a very close
watch on Commercial-Off-The-Shelf
(COTS) and Software-as-a-Service
(Saas) offerings. We understand this problem and the tools available
to solve it. We also understand the limitations and applicability of
these tools within specific industries.

This article is a brief overview of our experience and capability. We
hope you find it useful. For more information, or for advice on your
pricing analysis project, don't hesitate to get in touch.

Also, the results oriented reader can skip the theory and jump to
directly to the case study below

Topics covered in this article:

Competitor Price Monitoring (CPM)

CPM is the shortest path to improved pricing. In fact, it's the
only option for collecting competitor prices and creating market
snapshots. The concept is simple: discover competing products and
record your competitor pricing at a fixed frequency. This has been
practiced by brick and mortar retailers for decades ("flyer
shopping", "tire kicking", etc). And you can be sure your e-commerce
competitors are using it too.

Benefits of CPM

The primary output of CPM is a rolling snapshot of the current
market conditions. Having the high, low, median, and average market
prices can obviously help you strategically position your offerings.
Over time, however, these market snapshots can be aggregated to
illustrate market trends. When performed for a long enough time
frame, CPM sheds light on your competitors' pricing strategies, such
as market penetration campaigns, discounting, promotions, and new
products.

With respect to internal pricing objectives, CPM allows
retailers to be priced, for example, "5% below market average" or
"10% less than the competitor X". Meeting objectives like this can be
critical to larger pricing campaigns, such as market penetration.
Moreover, the analysis of competitor price history can give insight
into the long term pricing objectives of competitors. This
information can be used to iteratively refine internal objectives.

CPM also has the benefit of being unintrusive, meaning it doesn't
need to "integrate" with your e-commerce store. This translates into
major savings on startup costs and development. In fact, small
e-commerce operations can easily get started with a 3rd
party vendor. Medium and large e-commerce operations, however, face
additional challenges.

Implementing CPM

Medium and large e-commerce operations present additional
challenges for CPM. These challenges are largely issues of scale.
However, complexity increases with certain requirements specific to
larger companies. Consider the following challenges:

Increased Products/SKUs - Today, even small online stores can have
10's of thousands of products. As of 2015, Amazon.com was selling 488
million products! The reality is, 3rd party vendors just don't
scale with a Long
Tail business model.

Market Growth - The writing is on the wall here: the entire
e-commerce market is growing steadily. Moreover, increasing SKUs
means competing in a much larger segment of that market. Scaling
with this growth - tracking existing competitors and discovering
new ones - is typically beyond the capability of SaaS vendors.

Dynamic
Pricing & A/B Tests - Many sites adjust prices in real-time,
based on individual customer history. Almost all sites are
conducting A/B Tests, distributing customers across different
versions of the same site. This effectively multiplies the effort
required to shop a competitor. In addition, it obfuscates the
overall price strategy.

Multi-Channel - In addition to dynamic stores, many retailers
are using multiple sales channels. Comparison Shopping Engines
(CSEs) are very common example of this. Again, the effort to
monitor prices is multiplied and the overall price strategy is
obfuscated.

The
aforementioned challenges are specific to CPM. However, medium and
large e-commerce operations are typically interested in a lot more
than just price data. Since effort is already being expended to
locate and collect competitor prices, it makes sense to collect the
additional data at the same time. This presents additional challenges
for SaaS.

Prices are Compact - Collecting and delivering price data is a low
bandwidth problem. Adhoc data collection, e.g. product reviews or
images, has a much larger footprint. To put this in perspective, a
compressed CSV file containing 50,000 product prices occupies a few
hundred kilobytes of space. A single product page at Amazon.com,
however, is several megabytes. SaaS simply can't deliver the wealth
of competitor data available on the web.

Moving Targets - Competitors adjust their content and
format constantly. With respect to collecting pricing data, this is
a manageable problem for SaaS. However, it doesn't scale for adhoc
data collection. The more data collected, the harder it becomes to
stay in sync with competitor changes.

RJB Approach

Our approach to high volume CPM makes 2 major departures from
main stream solutions. First, the entire process is workflow driven,
where tasks are delegated to the most suitable 'agent'. The key is to
find the optimal human-machine collaboration point, maximizing
accuracy and throughput, while minimizing cost. This collaboration
point isn't fixed, it changes with the limits of machine
intelligence.

This leads to our second departure - machine learning.
Automation is the goal, as it saves time and money. However, machine
limitations force humans to occasionally step into the workflow. This
presents a great opportunity to train the machine. Over time, the
system drives itself toward full automation.

Before we describe the process in detail, here's a conceptual
view of round trip CPM, from the inclusion of a new product to the
generation of optimized prices:

1. Product Details

The goal of this step is to collect enough product data to
produce viable search keywords. Our best results have come from
combining multiple data collection techniques. For example, product
identifiers (UPC, EAN, ISBN, etc.), descriptions, specifications,
etc. can often be 'scraped' from the production e-commerce site by
machine agents. These results can be augmented by internal users and
offline product data. Crowd sourcing can also be introduced to
capture alternate product descriptions, word associations, etc. With
a workflow based architecture, it's incredibly easy for humans and
machines to collaborate on product details.

2. Match Candidates

This step produces a collection of URLs for potential competing
products. Search keywords from the previous step are executed on
general purpose engines (Google, Bing, Yahoo, etc.), and on known
competitor sites. Search results are crawled in a 'drill down'
fashion until product detail pages are reached. Again, this is highly
automated. Humans only step in when the machine can't make sense of
something; and when this happens, the machine learns from the human
interaction.

3. Product Match

This step produces a match confidence for each match candidate
identified in the previous step. It also identifies competitors by
association i.e. when a product match is found on a new competitor.
While this is an easy task for humans, it's the most difficult step
to automate in CPM. Our approach to automatic matching is private
intellectual property. However, as mentioned above, when a human
steps into this workflow, the machine learns from it.

4. Price Collection

This step produces price data for each competing product
identified in the previous step. It's the second most difficult step
to automate in CPM, as the complexity varies across markets and
competitors. For example, Business-to-Business (B2B) operations often
have tiered pricing. Business-to-Customer (B2C) operations often have
hidden fees, variable shipping costs, etc. that are not available
until checkout confirmation. In either case, machines can eventually
be taught to 'scrape' this data.

5. Price Generation

This step produces new 'optimal' prices. It's almost always
automated. Price generation is not exclusive to CPM, however, CPM
unlocks certain pricing objectives mentioned above. If these objectives meet your
needs, you can stop here and generate prices. However, as we describe
below, additional methods can be used to collect
more data and unlock new pricing strategies.

A Few Remarks

Steps 1 to 3 are discovery activities, and they are performed
once when a product is included in CPM. They may be performed
periodically, but with a reasonably low frequency. Steps 4 & 5
are operational activities. They are performed very frequently,
sometimes as often as hourly.

Price Elasticity of Demand (PED)

While CPM can deliver market snapshots and competitor pricing trends,
it says nothing about the affect of price changes on sales demand.
This relationship, formally known as Price
Elasticity of Demand (PED), is the key to unlocking additional goal
driven price strategies. It can be summarized as follows: holding
other factors equal, if the price is changed by X%, sales demand will
change by Y%.

The results oriented reader will find an example PED curve in the
case study below.

Benefits of PED

The first pricing objective unlocked by PED is Revenue
Optimization. This is achieved by simply choosing the point on the
PED curve where sales demand is highest. It's very straight forward
to implement, however, with the exception of market penetration
campaigns, it's a naive business objective, as it doesn't account for
sales margins. Simply stated, revenue optimization will almost always
increase sales and decrease profit per sale.

A more sophisticated PED based approach is Profit Optimization.
This method factors in sales costs to generate prices that maximize
profit. Profit optimization can also deliver new business insight.
For example, an optimal solution could be to sell less units at a
much greater margin, an approach that isn't always intuitive.

For many retailers, profit optimization is almost as simple as
revenue optimization, because calculating costs is simple. However,
in certain industries, including many B2B niches, profit optimization
involves computing complex costs. For example, B2B retailers often
receive volume based (i.e. tiered) prices from their suppliers. They
also offer volume based prices to their customers. Profit
optimization must factor in all fixed and variable elements to
deliver an accurate solution.

Determining PED

Unlike CPM, which evaluates the external competition, PED is
determined by measuring the performance of your own products and
prices. As a result, accurately determining PED can be more intrusive
than CPM. Consider the following options:

Sales History - A baseline PED relationship can be derived
from existing sales history. Like CPM, this has the benefit of
being completely unintrusive. Unfortunately it's not very accurate.
Sales history typically doesn't contain context information, such
as active promotions or market conditions at the time of sale. In
short, this approach can't satisfy the "holding other factors
equal" requirement.

Market Research, Surveys & Crowd Sourcing - PED can also be
determined via market research. This is a middle ground approach,
that mitigates some of the contextual issues of sales history
analysis. It also remains unintrusive and it's reasonably easy to
deploy. For example, deploying a pricing survey to Amazon Mechanical
Turk is relatively quick and easy. On the other hand, data quality
is very dependent on survey design and crowd segmentation. A survey
must ask the right people the right questions, or the data can't be
trusted. It's also important to keep in mind that survey
participants have very little "skin in the game". Asking an
individual what they would pay is different than having them pay
it.

A/B
Testing - A practical way to determine PED is by real-time pricing
experiments. Using A/B Testing, customers are distributed across
multiple versions of the store, where each version has different
pricing. The test is run for a fixed length of time, and the
performance (i.e. sales) of the each store version is evaluated.
A/B price testing works because it satisfies the "holding other
factors equal" requirement, while overcoming the "skin in the game"
deficiency of market research approaches. On the other hand, A/B
price testing is more difficult to deploy, particularly if your
e-commerce platform doesn't support it. It's also important to note
that these price tests will affect your actual sales, and they can
last for weeks or months when slow sellers are measured (i.e. Long
Tail sales).

A Case Study in the Promotional Products
Industry

"With RJB's solution we were able to automatically optimize our
prices, while reducing labor costs and delivering more to the bottom
line." Director of Merchandising -
E-commerce Retailer

RJB was involved in conception through to delivery of a new
pricing system for a large e-commerce retailer. The customer is a
multi-million dollar B2B retailer of promotional products. They offer
15000+ products, with different charge models for various custom logo
methods. They also offer tiered pricing to customers and receive
volume based supplier costs. They ship internationally. Due the the
financial nature of this case, the customer chose not to be
identified.

"As a combination of Phases 1 & 2, Gross Profit
increased by 21.6%"

Phase 1

Objective

Increase Gross Revenue with a Market Penetration campaign
to be the lowest price in the industry.

RJB designed and implemented an in-house CPM system. The
system includes a software platform for identifying competitors
and competing products, as well as for regular daily price
monitoring of known products.

Result

The customer maintained the lowest price in the industry
for 9 months. As a result:

Gross Revenue increased by 20.9%

Gross Margin decreased by 3%, however, Gross Profit
increased by 9.7% due the increased sales volume.

CPM data (ongoing from Phase 1), to alert management of
products/sales tiers that are no longer competitive due to
profit optimization.

Variable product costs derived from volume based
pricing.

The system produces optimal prices and sales price tiers for all
products in the e-commerce store. These prices are staged daily
back into the online store.

Result

After a year of Profit Optimization:

Gross Profit increased by an additional 10.8%

As a combination of Phases 1 & 2, Gross Profit
increased by 21.6%

As you can imagine, this project was considered a major
success. The customer not only exceeded target market share gains,
they retained these gains through a subsequent year of profit
optimized prices, generating millions in top line revenue and an
increase in gross profitability of 21.6% across sales of all
products.

Example PED Curve

Here's a example PED curve from the case study project. NOTE:
on this project, PED was commonly referred to as "Price Sensitivity":

There are a few interesting details to note here. First, the
units used to describe this PED relationship are relative. The X-axis
represents the percentage markup or markdown from the competitor
price average. The Y-axis represents the increase or decrease in the
number of orders, as a multiplier from the baseline expected number
of orders. Keep in mind that this is not the only way to describe the
PED relationship. However, this was the method used in the case study
project.

Looking at the curve itself, we can see that an Order
Multiplier of 1 (i.e. the baseline number of orders) corresponds to a
markdown of 20%. This is due to the Phase 1 market penetration
campaign. After the campaign, the standard price for this retailer
was 20% below market average.

Final Remarks

Although it doesn't directly pertain to CPM, it's important to note
that there was an immediate Phase 3 follow up to this project, where
RJB was tasked with collecting an additional 18-20 pieces of data
(e.g. shipping costs, decoration charges, etc.) while performing CPM.
Fortunately, the system architecture allowed us to keep up with the increased data
volume and moving targets.